code: def get_t5_flash_attention_forward():
      try:
          from xformers.ops import (
              memory_efficient_attention as me_attention,
          )
      except:
          raise ImportError(
              "Error: xformers module is not installed. Please install it to use flash attention."
          )
      from transformers.models.t5.modeling_t5 import T5Attention
  
      def forward(
          self: T5Attention,
          hidden_states: torch.Tensor,
          mask: Optional[torch.Tensor] = None,
          key_value_states: Optional[torch.Tensor] = None,
          position_bias: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          query_length: Optional[int] = None,
          use_cache: bool = False,
          output_attentions: bool = False,
      ) -> Tuple[
          torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]
      ]:
          """
          Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
          """
          # Input is (batch_size, seq_length, dim)
          # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
          # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
          batch_size, seq_length = hidden_states.shape[:2]
  
          real_seq_length = seq_length
  
          if past_key_value is not None:
              if len(past_key_value) != 2:
                  raise ValueError(
                      f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
                  )
              real_seq_length += (
                  past_key_value[0].shape[2]
                  if query_length is None
                  else query_length
              )
  
          key_length = (
              real_seq_length
              if key_value_states is None
              else key_value_states.shape[1]
          )
  
          def shape(states):
              """projection"""
              return states.view(
                  batch_size, -1, self.n_heads, self.key_value_proj_dim
              )
  
          def unshape(states):
              """reshape"""
              return states.view(batch_size, -1, self.inner_dim)
  
          def project(
              hidden_states,
              proj_layer,
              key_value_states,
              past_key_value,
          ):
              """projects hidden states correctly to key/query states"""
              if key_value_states is None:
                  # self-attn
                  # (batch_size, n_heads, seq_length, dim_per_head)
                  hidden_states = shape(proj_layer(hidden_states))
              elif past_key_value is None:
                  # cross-attn
                  # (batch_size, n_heads, seq_length, dim_per_head)
                  hidden_states = shape(proj_layer(key_value_states))
  
              if past_key_value is not None:
                  if key_value_states is None:
                      # self-attn
                      # (batch_size, n_heads, key_length, dim_per_head)
                      hidden_states = torch.cat(
                          [past_key_value, hidden_states], dim=1
                      )
                  elif (
                      past_key_value.shape[1]
                      != key_value_states.shape[1]
                  ):
                      # checking that the `sequence_length` of the `past_key_value` is the same as
                      # the provided `key_value_states` to support prefix tuning
                      # cross-attn
                      # (batch_size, n_heads, seq_length, dim_per_head)
                      hidden_states = shape(
                          proj_layer(key_value_states)
                      )
                  else:
                      # cross-attn
                      hidden_states = past_key_value
              return hidden_states
  
          # get query states
          query_states = shape(
              self.q(hidden_states)
          )  # (batch_size, n_heads, seq_length, dim_per_head)
  
          # get key/value states
          key_states = project(
              hidden_states,
              self.k,
              key_value_states,
              past_key_value[0] if past_key_value is not None else None,
          )
          value_states = project(
              hidden_states,
              self.v,
              key_value_states,
              past_key_value[1] if past_key_value is not None else None,
          )
  
          if position_bias is None:
              if not self.has_relative_attention_bias:
                  position_bias = torch.zeros(
                      (1, self.n_heads, real_seq_length, key_length),
                      device=query_states.device,
                      dtype=query_states.dtype,
                  )
                  if self.gradient_checkpointing and self.training:
                      position_bias.requires_grad = True
              else:
                  position_bias = self.compute_bias(
                      real_seq_length,
                      key_length,
                      device=query_states.device,
                  )
  
              # if key and values are already calculated
              # we want only the last query position bias
              if past_key_value is not None:
                  position_bias = position_bias[
                      :, :, -hidden_states.size(1) :, :
                  ]
  
              if mask is not None:
                  position_bias = (
                      position_bias + mask
                  )  # (batch_size, n_heads, seq_length, key_length)
  
          if self.pruned_heads:
              mask = torch.ones(position_bias.shape[1])
              mask[list(self.pruned_heads)] = 0
              position_bias_masked = position_bias[:, mask.bool()]
          else:
              position_bias_masked = position_bias
  
          position_bias_masked = position_bias_masked.contiguous()
          attn_output = me_attention(
              query_states,
              key_states,
              value_states,
              attn_bias=position_bias_masked,
              p=self.dropout,
              scale=1.0,
          )
          attn_output = unshape(attn_output)
          attn_output = self.o(attn_output)
  
          present_key_value_state = (
              (key_states, value_states)
              if (self.is_decoder and use_cache)
              else None
          )
  
          outputs = (
              (attn_output,)
              + (present_key_value_state,)
              + (position_bias,)
          )
  
          return outputs
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/t5.py
imports: import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import CrossEntropyLoss
  from torch.utils.checkpoint import checkpoint
  from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 1 for calling a global function with example usage: get_t5_flash_attention_forward() and returns: forward, outputs, states, states, hidden_states